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<div class="section" id="module-apache_beam.ml.inference.xgboost_inference">
<span id="apache-beam-ml-inference-xgboost-inference-module"></span><h1>apache_beam.ml.inference.xgboost_inference module<a class="headerlink" href="#module-apache_beam.ml.inference.xgboost_inference" title="Permalink to this headline"></a></h1>
<dl class="class">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler">
<em class="property">class </em><code class="descclassname">apache_beam.ml.inference.xgboost_inference.</code><code class="descname">XGBoostModelHandler</code><span class="sig-paren">(</span><em>model_class: Union[Callable[[...], xgboost.core.Booster], Callable[[...], xgboost.sklearn.XGBModel]], model_state: str, inference_fn: Callable[[Sequence[object], Union[xgboost.core.Booster, xgboost.sklearn.XGBModel], Optional[Dict[str, Any]]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_xgboost_inference_fn&gt;, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler" title="apache_beam.ml.inference.base.ModelHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.base.ModelHandler</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/abc.html#abc.ABC" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></a></p>
<p>Implementation of the ModelHandler interface for XGBoost.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span>
<span class="n">XGBoostModelHandler</span><span class="p">(</span>
<span class="n">model_class</span><span class="o">=</span><span class="s2">&quot;XGBoost Model Class&quot;</span><span class="p">,</span>
<span class="n">model_state</span><span class="o">=</span><span class="s2">&quot;my_model_state.json&quot;</span><span class="p">)))</span>
</pre></div>
</div>
<p>See <a class="reference external" href="https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html">https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html</a>
for details</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_class</strong> – class of the XGBoost model that defines the model
structure.</li>
<li><strong>model_state</strong> – path to a json file that contains the model’s
configuration.</li>
<li><strong>inference_fn</strong> – the inference function to use during RunInference.
default=default_xgboost_inference_fn</li>
<li><strong>kwargs</strong> – ‘env_vars’ can be used to set environment variables
before loading the model.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p><strong>Supported Versions:</strong> RunInference APIs in Apache Beam have been tested
with XGBoost 1.6.0 and 1.7.0</p>
<p>XGBoost 1.0.0 introduced support for using JSON to save and load
XGBoost models. XGBoost 1.6.0, additional support for Universal Binary JSON.
It is recommended to use a model trained in XGBoost 1.6.0 or higher.
While you should be able to load models created in older versions, there
are no guarantees this will work as expected.</p>
<p>This class is the superclass of all the various XGBoostModelhandlers
and should not be instantiated directly. (See instead
XGBoostModelHandlerNumpy, XGBoostModelHandlerPandas, etc.)</p>
<dl class="method">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler.load_model">
<code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; Union[xgboost.core.Booster, xgboost.sklearn.XGBModel]<a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandler.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler.load_model" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler.get_metrics_namespace">
<code class="descname">get_metrics_namespace</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; str<a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandler.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler.get_metrics_namespace" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="class">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerNumpy">
<em class="property">class </em><code class="descclassname">apache_beam.ml.inference.xgboost_inference.</code><code class="descname">XGBoostModelHandlerNumpy</code><span class="sig-paren">(</span><em>model_class: Union[Callable[[...], xgboost.core.Booster], Callable[[...], xgboost.sklearn.XGBModel]], model_state: str, inference_fn: Callable[[Sequence[object], Union[xgboost.core.Booster, xgboost.sklearn.XGBModel], Optional[Dict[str, Any]]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_xgboost_inference_fn&gt;, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerNumpy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerNumpy" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler" title="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler</span></code></a></p>
<p>Implementation of the ModelHandler interface for XGBoost
using numpy arrays as input.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span>
<span class="n">XGBoostModelHandlerNumpy</span><span class="p">(</span>
<span class="n">model_class</span><span class="o">=</span><span class="s2">&quot;XGBoost Model Class&quot;</span><span class="p">,</span>
<span class="n">model_state</span><span class="o">=</span><span class="s2">&quot;my_model_state.json&quot;</span><span class="p">)))</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_class</strong> – class of the XGBoost model that defines the model
structure.</li>
<li><strong>model_state</strong> – path to a json file that contains the model’s
configuration.</li>
<li><strong>inference_fn</strong> – the inference function to use during RunInference.
default=default_xgboost_inference_fn</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Implementation of the ModelHandler interface for XGBoost.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span>
<span class="n">XGBoostModelHandler</span><span class="p">(</span>
<span class="n">model_class</span><span class="o">=</span><span class="s2">&quot;XGBoost Model Class&quot;</span><span class="p">,</span>
<span class="n">model_state</span><span class="o">=</span><span class="s2">&quot;my_model_state.json&quot;</span><span class="p">)))</span>
</pre></div>
</div>
<p>See <a class="reference external" href="https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html">https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html</a>
for details</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_class</strong> – class of the XGBoost model that defines the model
structure.</li>
<li><strong>model_state</strong> – path to a json file that contains the model’s
configuration.</li>
<li><strong>inference_fn</strong> – the inference function to use during RunInference.
default=default_xgboost_inference_fn</li>
<li><strong>kwargs</strong> – ‘env_vars’ can be used to set environment variables
before loading the model.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p><strong>Supported Versions:</strong> RunInference APIs in Apache Beam have been tested
with XGBoost 1.6.0 and 1.7.0</p>
<p>XGBoost 1.0.0 introduced support for using JSON to save and load
XGBoost models. XGBoost 1.6.0, additional support for Universal Binary JSON.
It is recommended to use a model trained in XGBoost 1.6.0 or higher.
While you should be able to load models created in older versions, there
are no guarantees this will work as expected.</p>
<p>This class is the superclass of all the various XGBoostModelhandlers
and should not be instantiated directly. (See instead
XGBoostModelHandlerNumpy, XGBoostModelHandlerPandas, etc.)</p>
<dl class="method">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerNumpy.run_inference">
<code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[numpy.ndarray], model: Union[xgboost.core.Booster, xgboost.sklearn.XGBModel], inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[apache_beam.ml.inference.base.PredictionResult]<a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerNumpy.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerNumpy.run_inference" title="Permalink to this definition"></a></dt>
<dd><p>Runs inferences on a batch of 2d numpy arrays.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>batch</strong> – A sequence of examples as 2d numpy arrays. Each
row in an array is a single example. The dimensions
must match the dimensions of the data used to train
the model.</li>
<li><strong>model</strong> – XGBoost booster or XBGModel (sklearn interface). Must
implement predict(X). Where the parameter X is a 2d numpy array.</li>
<li><strong>inference_args</strong> – Any additional arguments for an inference.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">An Iterable of type PredictionResult.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerNumpy.get_num_bytes">
<code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[numpy.ndarray]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerNumpy.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerNumpy.get_num_bytes" title="Permalink to this definition"></a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The number of bytes of data for a batch.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerPandas">
<em class="property">class </em><code class="descclassname">apache_beam.ml.inference.xgboost_inference.</code><code class="descname">XGBoostModelHandlerPandas</code><span class="sig-paren">(</span><em>model_class: Union[Callable[[...], xgboost.core.Booster], Callable[[...], xgboost.sklearn.XGBModel]], model_state: str, inference_fn: Callable[[Sequence[object], Union[xgboost.core.Booster, xgboost.sklearn.XGBModel], Optional[Dict[str, Any]]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_xgboost_inference_fn&gt;, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerPandas"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerPandas" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler" title="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler</span></code></a></p>
<p>Implementation of the ModelHandler interface for XGBoost
using pandas dataframes as input.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span>
<span class="n">XGBoostModelHandlerPandas</span><span class="p">(</span>
<span class="n">model_class</span><span class="o">=</span><span class="s2">&quot;XGBoost Model Class&quot;</span><span class="p">,</span>
<span class="n">model_state</span><span class="o">=</span><span class="s2">&quot;my_model_state.json&quot;</span><span class="p">)))</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_class</strong> – class of the XGBoost model that defines the model
structure.</li>
<li><strong>model_state</strong> – path to a json file that contains the model’s
configuration.</li>
<li><strong>inference_fn</strong> – the inference function to use during RunInference.
default=default_xgboost_inference_fn</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Implementation of the ModelHandler interface for XGBoost.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span>
<span class="n">XGBoostModelHandler</span><span class="p">(</span>
<span class="n">model_class</span><span class="o">=</span><span class="s2">&quot;XGBoost Model Class&quot;</span><span class="p">,</span>
<span class="n">model_state</span><span class="o">=</span><span class="s2">&quot;my_model_state.json&quot;</span><span class="p">)))</span>
</pre></div>
</div>
<p>See <a class="reference external" href="https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html">https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html</a>
for details</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_class</strong> – class of the XGBoost model that defines the model
structure.</li>
<li><strong>model_state</strong> – path to a json file that contains the model’s
configuration.</li>
<li><strong>inference_fn</strong> – the inference function to use during RunInference.
default=default_xgboost_inference_fn</li>
<li><strong>kwargs</strong> – ‘env_vars’ can be used to set environment variables
before loading the model.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p><strong>Supported Versions:</strong> RunInference APIs in Apache Beam have been tested
with XGBoost 1.6.0 and 1.7.0</p>
<p>XGBoost 1.0.0 introduced support for using JSON to save and load
XGBoost models. XGBoost 1.6.0, additional support for Universal Binary JSON.
It is recommended to use a model trained in XGBoost 1.6.0 or higher.
While you should be able to load models created in older versions, there
are no guarantees this will work as expected.</p>
<p>This class is the superclass of all the various XGBoostModelhandlers
and should not be instantiated directly. (See instead
XGBoostModelHandlerNumpy, XGBoostModelHandlerPandas, etc.)</p>
<dl class="method">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerPandas.run_inference">
<code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[pandas.core.frame.DataFrame], model: Union[xgboost.core.Booster, xgboost.sklearn.XGBModel], inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[apache_beam.ml.inference.base.PredictionResult]<a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerPandas.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerPandas.run_inference" title="Permalink to this definition"></a></dt>
<dd><p>Runs inferences on a batch of pandas dataframes.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>batch</strong> – A sequence of examples as pandas dataframes. Each
row in a dataframe is a single example. The dimensions
must match the dimensions of the data used to train
the model.</li>
<li><strong>model</strong> – XGBoost booster or XBGModel (sklearn interface). Must
implement predict(X). Where the parameter X is a pandas dataframe.</li>
<li><strong>inference_args</strong> – Any additional arguments for an inference.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">An Iterable of type PredictionResult.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerPandas.get_num_bytes">
<code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[pandas.core.frame.DataFrame]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerPandas.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerPandas.get_num_bytes" title="Permalink to this definition"></a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The number of bytes of data for a batch of Numpy arrays.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerSciPy">
<em class="property">class </em><code class="descclassname">apache_beam.ml.inference.xgboost_inference.</code><code class="descname">XGBoostModelHandlerSciPy</code><span class="sig-paren">(</span><em>model_class: Union[Callable[[...], xgboost.core.Booster], Callable[[...], xgboost.sklearn.XGBModel]], model_state: str, inference_fn: Callable[[Sequence[object], Union[xgboost.core.Booster, xgboost.sklearn.XGBModel], Optional[Dict[str, Any]]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_xgboost_inference_fn&gt;, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerSciPy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerSciPy" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler" title="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler</span></code></a></p>
<p>Implementation of the ModelHandler interface for XGBoost
using scipy matrices as input.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span>
<span class="n">XGBoostModelHandlerSciPy</span><span class="p">(</span>
<span class="n">model_class</span><span class="o">=</span><span class="s2">&quot;XGBoost Model Class&quot;</span><span class="p">,</span>
<span class="n">model_state</span><span class="o">=</span><span class="s2">&quot;my_model_state.json&quot;</span><span class="p">)))</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_class</strong> – class of the XGBoost model that defines the model
structure.</li>
<li><strong>model_state</strong> – path to a json file that contains the model’s
configuration.</li>
<li><strong>inference_fn</strong> – the inference function to use during RunInference.
default=default_xgboost_inference_fn</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Implementation of the ModelHandler interface for XGBoost.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span>
<span class="n">XGBoostModelHandler</span><span class="p">(</span>
<span class="n">model_class</span><span class="o">=</span><span class="s2">&quot;XGBoost Model Class&quot;</span><span class="p">,</span>
<span class="n">model_state</span><span class="o">=</span><span class="s2">&quot;my_model_state.json&quot;</span><span class="p">)))</span>
</pre></div>
</div>
<p>See <a class="reference external" href="https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html">https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html</a>
for details</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_class</strong> – class of the XGBoost model that defines the model
structure.</li>
<li><strong>model_state</strong> – path to a json file that contains the model’s
configuration.</li>
<li><strong>inference_fn</strong> – the inference function to use during RunInference.
default=default_xgboost_inference_fn</li>
<li><strong>kwargs</strong> – ‘env_vars’ can be used to set environment variables
before loading the model.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p><strong>Supported Versions:</strong> RunInference APIs in Apache Beam have been tested
with XGBoost 1.6.0 and 1.7.0</p>
<p>XGBoost 1.0.0 introduced support for using JSON to save and load
XGBoost models. XGBoost 1.6.0, additional support for Universal Binary JSON.
It is recommended to use a model trained in XGBoost 1.6.0 or higher.
While you should be able to load models created in older versions, there
are no guarantees this will work as expected.</p>
<p>This class is the superclass of all the various XGBoostModelhandlers
and should not be instantiated directly. (See instead
XGBoostModelHandlerNumpy, XGBoostModelHandlerPandas, etc.)</p>
<dl class="method">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerSciPy.run_inference">
<code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[scipy.sparse._csr.csr_matrix], model: Union[xgboost.core.Booster, xgboost.sklearn.XGBModel], inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[apache_beam.ml.inference.base.PredictionResult]<a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerSciPy.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerSciPy.run_inference" title="Permalink to this definition"></a></dt>
<dd><p>Runs inferences on a batch of SciPy sparse matrices.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>batch</strong> – A sequence of examples as Scipy sparse matrices.
The dimensions must match the dimensions of the data
used to train the model.</li>
<li><strong>model</strong> – XGBoost booster or XBGModel (sklearn interface). Must implement
predict(X). Where the parameter X is a SciPy sparse matrix.</li>
<li><strong>inference_args</strong> – Any additional arguments for an inference.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">An Iterable of type PredictionResult.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerSciPy.get_num_bytes">
<code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[scipy.sparse._csr.csr_matrix]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerSciPy.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerSciPy.get_num_bytes" title="Permalink to this definition"></a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The number of bytes of data for a batch.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerDatatable">
<em class="property">class </em><code class="descclassname">apache_beam.ml.inference.xgboost_inference.</code><code class="descname">XGBoostModelHandlerDatatable</code><span class="sig-paren">(</span><em>model_class: Union[Callable[[...], xgboost.core.Booster], Callable[[...], xgboost.sklearn.XGBModel]], model_state: str, inference_fn: Callable[[Sequence[object], Union[xgboost.core.Booster, xgboost.sklearn.XGBModel], Optional[Dict[str, Any]]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_xgboost_inference_fn&gt;, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerDatatable"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerDatatable" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler" title="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler</span></code></a></p>
<p>Implementation of the ModelHandler interface for XGBoost
using datatable dataframes as input.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span>
<span class="n">XGBoostModelHandlerDatatable</span><span class="p">(</span>
<span class="n">model_class</span><span class="o">=</span><span class="s2">&quot;XGBoost Model Class&quot;</span><span class="p">,</span>
<span class="n">model_state</span><span class="o">=</span><span class="s2">&quot;my_model_state.json&quot;</span><span class="p">)))</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_class</strong> – class of the XGBoost model that defines the model
structure.</li>
<li><strong>model_state</strong> – path to a json file that contains the model’s
configuration.</li>
<li><strong>inference_fn</strong> – the inference function to use during RunInference.
default=default_xgboost_inference_fn</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Implementation of the ModelHandler interface for XGBoost.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span>
<span class="n">XGBoostModelHandler</span><span class="p">(</span>
<span class="n">model_class</span><span class="o">=</span><span class="s2">&quot;XGBoost Model Class&quot;</span><span class="p">,</span>
<span class="n">model_state</span><span class="o">=</span><span class="s2">&quot;my_model_state.json&quot;</span><span class="p">)))</span>
</pre></div>
</div>
<p>See <a class="reference external" href="https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html">https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html</a>
for details</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_class</strong> – class of the XGBoost model that defines the model
structure.</li>
<li><strong>model_state</strong> – path to a json file that contains the model’s
configuration.</li>
<li><strong>inference_fn</strong> – the inference function to use during RunInference.
default=default_xgboost_inference_fn</li>
<li><strong>kwargs</strong> – ‘env_vars’ can be used to set environment variables
before loading the model.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p><strong>Supported Versions:</strong> RunInference APIs in Apache Beam have been tested
with XGBoost 1.6.0 and 1.7.0</p>
<p>XGBoost 1.0.0 introduced support for using JSON to save and load
XGBoost models. XGBoost 1.6.0, additional support for Universal Binary JSON.
It is recommended to use a model trained in XGBoost 1.6.0 or higher.
While you should be able to load models created in older versions, there
are no guarantees this will work as expected.</p>
<p>This class is the superclass of all the various XGBoostModelhandlers
and should not be instantiated directly. (See instead
XGBoostModelHandlerNumpy, XGBoostModelHandlerPandas, etc.)</p>
<dl class="method">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerDatatable.run_inference">
<code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[datatable.Frame], model: Union[xgboost.core.Booster, xgboost.sklearn.XGBModel], inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[apache_beam.ml.inference.base.PredictionResult]<a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerDatatable.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerDatatable.run_inference" title="Permalink to this definition"></a></dt>
<dd><p>Runs inferences on a batch of datatable dataframe.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>batch</strong> – A sequence of examples as datatable dataframes. Each
row in a dataframe is a single example. The dimensions
must match the dimensions of the data used to train
the model.</li>
<li><strong>model</strong> – XGBoost booster or XBGModel (sklearn interface). Must implement
predict(X). Where the parameter X is a datatable dataframe.</li>
<li><strong>inference_args</strong> – Any additional arguments for an inference.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">An Iterable of type PredictionResult.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerDatatable.get_num_bytes">
<code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[datatable.Frame]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/xgboost_inference.html#XGBoostModelHandlerDatatable.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerDatatable.get_num_bytes" title="Permalink to this definition"></a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The number of bytes of data for a batch.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
</div>
</div>
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